… Many MR post-processing tasks can be formulated as image-to-image transformation
problems where deep learning models are used to capture the nonlinear relationships between …

… 1) We present a color correction method based on a subinterval linear transformation and a
variational model (SLVC). The enhanced method removes the color cast and improves color …

… -DCE, to transform the image enhancement problem into a … for low-light image enhancement
and used unpaired images for … -light image enhancement and other image restoration tasks. …

… To alleviate these effects, a number of low-light image enhancement (LLIE) methods have
been proposed to transform a given low-light image into a highquality image with appropriate …

… image, and a spatial feature transform layer, which integrate the histogram equalization
prior into intermediate feature maps. Given that the SFT layer is proposed by [20], the detailed …

… In the early stages, image restoration-based methods relied on … Unlike image restoration
methods, image enhancement-based … The wavelet transform of an image can be expressed as: …

… Histogram equalization-based methods, which transform the low-light image from a relatively
concentrated gray-scale range to a uniform distribution over the entire range, were popular …

… After the above operations, we transform the images with replaced textures back to the
RGB colorspace. It can be observed that the results in the YCbCr colorspace attain the highest …

… The convolution net consists of three steps: feature map transformation, entropy-inspired
adaptation and co-attention fusion. Feature map transformation: The image feature from the …

… image enhancement task and propose a physically explainable and generative diffusion
model for low-light image enhancement, … in the low-light image through the generative network. …