the described approach is to analyse the gradient represented by pixels at the periphery of a corrected image region in order to evaluate the efficacy of the correction process in that region . this is illustrated in fig1 which shows an array of pixels within an image , for example a single film frame or a video field derived from a film frame . two separate areas within the illustrated image area have been modified by some correction process ; the extents of these areas are shown by the outlines ( 1 ) and ( 2 ). the groups of pixels within either of these outlines may have been obscured by respective patches of dirt on the corresponding film frame , or , alternatively , either or both of the groups of pixels may represent respective image features that have been incorrectly identified as dirt patches by the correction process . the process of the invention analyses each region separately and so the corrected pixels must be allocated to discrete regions . thus , in fig1 , all the pixels within the outline ( 1 ) have been designated with the letter a or a ; and all the pixels within the outline ( 2 ) have been designated with the letter b or b . in the preferred embodiment only the peripheral pixels are used in the analysis , i . e . those pixels having a vertical or horizontal neighbour lying outside the region . these are the pixels designated with upper case letters ( a or b ) in fig1 . thus the region within the outline ( 1 ) is evaluated by analysis of the pixels a ; and the region within the outline ( 2 ) is evaluated by analysis of the pixels b . a suitable system is shown in fig2 . referring to fig2 a stream of pixels that describes an image , or a sequence of related images , is received at an input ( 201 ). these input pixels are processed in a correction process ( 202 ) that modifies the input pixels in order to eliminate artefacts ; for example , areas obscured by dirt particles or scratches . the modified pixels ( 203 ) at the output of the correction process ( 202 ) are compared with the input pixels in a pixel - comparator ( 204 ). each pixel from the input ( 201 ) is compared with the corresponding modified pixel ( 203 ); typically this will require the input pixels to be delayed in a compensating delay ( 205 ) so that corresponding pixels are presented simultaneously at the two inputs of the pixel - comparator ( 204 ). if the input ( 201 ) represents a sequence of film frames , then it is likely that the compensating delay ( 205 ) will need to be at least two frames long so as to ensure that the pixel - comparator ( 204 ) compares pixels that represent the same point in space and time . the pixel - comparator ( 204 ) identifies those pixels that have been changed by the correction process ( 202 ). this can be achieved by taking the difference between pixel values , typically luminance value differences , though any convenient measure that is affected by the correction process ( 202 ) can be used . having identified the pixels that have been corrected , they must be allocated to regions for analysis . the result from the pixel - comparator ( 204 ) is passed to a correction - region identification block ( 206 ), which identifies ( for each separate image ) every spatially - separated , contiguous region of modified pixels . the identification block ( 206 ) outputs correction - region data ( 207 ) that defines the constituent pixels of each spatially - separate , corrected region of the current image being processed . a suitable method is shown in the flow chart of fig3 . the pixels of the image are assessed in turn in some convenient order , for example a conventional raster - scan from top - left to bottom - right . in step ( 30 ) the first pixel is selected for analysis , in step ( 31 ) it is tested to see if it has been modified by the correction process ( for example by examining the relevant output from the comparator ( 204 ) in fig2 ). if it has not been modified , a test is made in step ( 32 ) to see whether all pixels have been assessed , and if not , the next pixel is selected for analysis in step ( 33 ). once the first modified pixel is found , a test is made in step ( 34 ) to see if any horizontally or vertically adjacent pixel has been allocated to a region . in the case of the first modified pixel to be evaluated no adjacent pixels will have been allocated , and the processing moves to step ( 35 ) in which the lowest of a set of unique ‘ region numbers ’ is allocated to the pixel . in subsequent iterations of the process the next highest region number is allocated each time step ( 35 ) is executed . after allocating a region number the processing moves to step ( 32 ). however , if one or more adjacent , allocated pixels is found at step ( 34 ), the current pixel is allocated the lowest of the region numbers of the adjacent pixels in step ( 36 ); and then , in step ( 37 ), any adjacent pixels are tested to see if any of them have already been allocated a higher region number , and , in step ( 38 ), all other pixels that have been allocated that , higher , region number ( regardless of adjacency ) are re - allocated to the lower region number ( i . e . the region number allocated to the current pixel ). once all the pixels of the image have been assessed , the test at step ( 32 ) ends the process and all separate , discrete regions will have had their constituent pixels allocated to respective , unique region numbers . returning to fig2 , the correction - region data ( 207 ) is input to two edge analysers ( 208 ) and ( 209 ), that evaluate , for each identified correction region of the current image , respective edge - difference measures for the input pixels ( 201 ), and for the corrected pixels ( 203 ). the edge analysers ( 208 ) and ( 209 ) operate identically and analyse identical image regions as defined by the correction - region data ( 207 ). first , pixels at the periphery of the region are identified ; these are pixels that have horizontally or vertically adjacent neighbours that are not part of the region being analysed . secondly , the magnitude of the pixel - value difference ( s ) between each of the identified pixels and its respective horizontally or vertically adjacent neighbour ( s ) that are not part of the region are accumulated . the accumulated value for all the identified peripheral pixels of a region forms the edge - difference measure for that region . this measure is representative of the gradient at the edge of the region and will be referred to as the edge - value for the region in the following description . fig4 shows a flow chart of a suitable method of evaluating this measure for a particular region . the edge - value value for the region is initialised to zero in step ( 400 ), and then the contributions of each of the pixels that have been allocated to the region are evaluated in turn , and the contributions are accumulated to achieve a resulting edge - value . the first pixel is selected in step ( 401 ), and in step ( 402 ) a test is made to see if the adjacent pixel above is outside the region ( i . e . that it has not been allocated to the region being assessed ). if the adjacent pixel above is outside the region , the pixel - value difference from the adjacent pixel above is calculated and its magnitude added to the edge - value accumulation in step ( 403 ). a similar test of the adjacent pixel to the left is made at step ( 404 ), and if the adjacent pixel to the left is outside the region , the magnitude of the pixel - value difference from the adjacent pixel to the left is added to the edge - value in step ( 405 ). the pixel to the right is tested at step ( 406 ), and if it is outside the region , the corresponding difference is added to the edge - value in step ( 407 ). finally , the pixel below is tested at step ( 408 ), and if the pixel is outside the region , the magnitude of its difference value is added in step ( 409 ); and in step ( 410 ), a test is made to see if all the pixels comprising the region have been evaluated . if not , the next pixel is selected in step ( 411 ) and the evaluation process repeated . once all the pixels of the region have been processed the total accumulated difference value is used to control the processing of that region as described below . returning to fig2 , if the correction process ( 202 ) has successfully corrected a particular region in a particular image , the edge - value corresponding to the original pixels of that region derived by the analyser ( 208 ) will be larger than the corresponding edge - value for the corrected pixels derived by the analyser ( 209 ). this is because the artefact , by definition , differs from its surrounding image pixels and therefore has a high edge - value ; but if the artefact is removed by the correction process ( 202 ) the edges of the artefact are also removed . if the correction process ( 202 ) mistakenly applies correction to an image area , the corrected area is likely to be different from its surrounding pixels , and this difference will contribute to the edge - value for that area as evaluated for the corrected image . however , the corresponding area of the uncorrected image is likely to have a lower edge - value because no ‘ artificial ’ pixel changes have been made . for each correction - region identified by the pixel - comparator ( 204 ), an edge - value comparator ( 210 ) compares the respective edge - values from the edge analyser ( 208 ) and the edge analyser ( 209 ). the difference between the two edge - values represents a correction cost / benefit measure for that region . if the edge - value from the edge analyser ( 209 ) is larger than the edge - value from the edge analyser ( 208 ), then it can be assumed that the correction process ( 202 ) has impaired that image region . and similarly , if the edge - value from the edge analyser ( 209 ) is smaller than the edge - value from the edge analyser ( 208 ), then it can be assumed that the correction process ( 202 ) has improved that image region . the result from the comparator ( 210 ) is used to control a changeover switch ( 211 ) that provides a stream of output pixels ( 212 ). normally the switch ( 211 ) routes the corrected pixels from the correction process ( 202 ), delayed by a compensating delay ( 213 ), to the output ( 212 ). however , when the edge - value comparator ( 210 ) determines that the correction process ( 202 ) has impaired the relevant image area , it causes the switch ( 211 ) to route the input pixels ( 201 ) that comprise that image area , delayed by compensating delays ( 205 ) and ( 214 ), to the output ( 212 ). the compensating delays ( 213 ) and ( 214 ) are chosen to ensure that the switch control signal from the edge - value comparator ( 210 ) and the inputs to the switch ( 211 ) are co - timed . typically each of these compensating delays are equal to the sum of the propagation delay of the respective edge analysis process , ( 208 ) or ( 209 ), and the propagation delay of the comparison process ( 210 ). the delay of the edge analysis process is usually at least a whole video field or frame period because an artefact may , in principle , extend over the full image height . there are a number of possible variations within the scope of the invention , some of which will now be described . it may not be necessary to compare corrected pixels with uncorrected pixels in order to identify the regions whose edges require analysis . some image correction processes derive a ‘ degree of impairment ’ measure for pixels or image regions and this measure may be used in place of the output from the comparator ( 204 ) in the system of fig2 . it may be helpful to compare this degree of impairment measure with a threshold , and use the result of that comparison to identify regions for edge analysis . the spatial sampling structure of the image may not be orthogonal ( as is the case in fig1 ). it may be helpful to use diagonally adjacent pixels in determining the edge - value for an image region . and , pixels more distant from the edge may be used to determine an alternative measure of gradient at the periphery of a region . for example , a set of pixels forming a filter aperture whose extent includes the position of the edge may be used to find a local gradient at a position near the edge . a summation of these gradient values around a particular image region can be used to obtain the gradient measure for that region . pixel values other than luminance values may be used to identify corrected regions or to evaluate the gradient measure for a region . chrominance differences could be used , or a combination of luminance and chrominance differences , such as taking the higher of the chrominance and luminance differences could be used . alternative methods of evaluating the gradient measures for regions can be used . for example , the pixels of the image to be evaluated can be assessed in turn in the sequence of a conventional raster scan , and when an edge pixel is encountered , the relevant edge difference value for that pixel added to the summation for the respective region . the switch ( 211 ) in the system of fig2 may be replaced by a cross - fader that outputs a weighted sum of corrected and uncorrected pixels , where the weights depend on a determined cost / benefit value for a region . the invention need not be implemented as a streaming process ; it may be implemented faster or slower than real - time , and may be performed on image files . the invention may be applied to a sub - sampled image , including individual fields that describe spatially sub - sampled versions of a common frame . the invention may be used to control the application a wide range of artefact correction processes . a simple example of a suitable dirt correction process for film - originated video material is to replace pixels in a current film frame that do not lie in the range of values bounded by the values of the equivalent pixels in the preceding and succeeding film frames by a value lying in the middle of that range .