and several other techniques in Table \[table:lol\] and Table \[table:fivek\] for the LoL [@wei2018deep] and MIT-Adobe FiveK [@mit_fivek] datasets, respectively. It can be seen that our MIRNet achieves significant improvements over previous approaches. Notably, when compared to the recent best methods, MIRNet obtains $3.27$ dB performance gain over KinD [@zhang2019kindling] on the LoL dataset and $0.69$ dB improvement over DeepUPE [@wang2019underexposed] on the Adobe-Fivek dataset.

We show visual results in Fig. \[Fig:qual\_lol\] and Fig. \[Fig:qual\_fivek\]. Compared to other techniques, our method generates enhanced images that are natural and vivid in appearance and have better global and local contrast.

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   ![Visual comparison of low-light enhancement approaches on the LoL dataset [@wei2018deep]. Our method reproduces image that is visually closer to the ground-truth in terms of brightness and global contrast.[]{data-label="Fig:qual_lol"}](Images/Enhancement/lol_images/lol_input "fig:"){width="24.40000%"}   ![Visual comparison of low-light enhancement approaches on the LoL dataset [@wei2018deep]. Our method reproduces image that is visually closer to the ground-truth in terms of brightness and global contrast.[]{data-label="Fig:qual_lol"}](Images/Enhancement/lol_images/lol_lime "fig:"){width="24.40000%"}     ![Visual comparison of low-light enhancement approaches on the LoL dataset [@wei2018deep]. Our method reproduces image that is visually closer to the ground-truth in terms of brightness and global contrast.[]{data-label="Fig:qual_lol"}](Images/Enhancement/lol_images/crm "fig:"){width="24.40000%"}      ![Visual comparison of low-light enhancement approaches on the 