our approach processes features at the original resolution in order to preserve spatial details, while effectively fuses contextual information from multiple parallel branches. Next, we briefly describe the representative methods for each of the studied problems. **Image denoising.** Classic denoising methods are mainly based on modifying transform coefficients [@yaroslavsky1996local; @donoho1995noising; @simoncelli1996noise] or averaging neighborhood pixels [@smith1997susan; @tomasi1998bilateral; @perona1990scale; @rudin1992nonlinear]. Although the classical methods perform well, the self-similarity [@efros1999texture] based algorithms, *e.g.*, NLM [@NLM] and BM3D [@BM3D], demonstrate promising denoising performance. Numerous patch-based algorithms that exploit redundancy (self-similarity) in images are later developed [@dong2012nonlocal; @WNNM; @mairal2009non; @hedjam2009markovian]. Recently, deep learning-based approaches [@MLP; @RIDNet; @Brooks2019; @Gharbi2016; @CBDNet; @N3Net; @DnCNN; @FFDNetPlus] make significant advances in image denoising, yielding favorable results than those of the hand-crafted methods.

**Super-resolution (SR).** Prior to the deep-learning era, numerous SR algorithms have been proposed based on the sampling theory [@keys1981cubic; @irani1991improving], edge-guided interpolation [@allebach1996edge; @li2001new; @zhang2006edge], natural image priors [@kim2010single; @xiong2010robust], patch-exemplars [@chang2004super; @freedman2011image] and sparse representations [@yang2010image; @yang2008image]. Currently, deep-learning techniques are actively being explored, as they provide dramatically improved results over conventional algorithms. The data-driven SR approaches differ according to their architecture designs [@wang2019deep; @anwar2019deep]. Early methods [@dong2014learning; @dong2015image] take a low-resolution (LR) image as input 