Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.
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In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark.
Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings.
Different from existing methods, which tend to accomplish the enhancement task directly, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps.
However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation.
However, as these techniques destroy spatial relationship with neighboring regions, performance can be deteriorated when using them to train algorithms designed for low level vision tasks (low light image enhancement, image dehazing, deblurring, etc.)
This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed.
One solution is to train a supervised deep neural network on many digital photos and the corresponding scanned photos.
Considering the diversity of degradation is mainly rooted in the strong correlation among pixels, we apply whitening to de-correlates activations across spatial dimensions for each instance in a mini-batch.