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Low-light images, i.e. the images captured in low-light conditions, sufferfrom very poor visibility caused by low contrast, color distortion andsignificant measurement noise. Low-light image enhancement is about improvingthe visibility of low-light images. As the measurement noise in low-lightimages is usually significant yet complex with spatially-varyingcharacteristic, how to handle the noise effectively is an important yetchallenging problem in low-light image enhancement. Based on the Retinexdecomposition of natural images, this paper proposes a deep learning method forlow-light image enhancement with a particular focus on handling the measurementnoise. The basic idea is to train a neural network to generate a set ofpixel-wise operators for simultaneously predicting the noise and theillumination layer, where the operators are defined in the bilateral space.Such an integrated approach allows us to have an accurate prediction of thereflectance layer in the presence of significant spatially-varying measurementnoise. Extensive experiments on several benchmark datasets have shown that theproposed method is very competitive to the state-of-the-art methods, and hassignificant advantage over others when processing images captured in extremelylow lighting conditions.

Deep Bilateral Retinex for Low-Light Image Enhancement