TPE: Lightweight Transformer Photo Enhancement Based on Curve Adjustment

Ma, Tian; An, Jinpeng; Xi, Runtao; Yang, Jiayi; Lyu, Jiahao; Li, Fanhui

doi:10.1109/access.2022.3191416

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The literature [17] designs a generative adversarial network containing dual attention units that can effectively inhibit the artifacts and color reproduction bias generated during the enhancement. Transformer Photo Enhancement (TPE) [18] uses a pure transformer architecture to implement image enhancement based on multi-stage curve adjustment. Retinex based deep unfolding network (URetinex-Net) [19] decomposes the input image by designing a continuous optimization model with mutual feedback.…”

Section: Fully Supervised Methodsmentioning

confidence: 99%

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Ma,

Fu,

Yang

et al. 2023

CMC

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Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the first stage, we design a multi-scale feature extraction module based on Retinex theory, capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images. In the second stage, an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features, and the generated images are fused with the original input images to complete the low-light image enhancement. Experiments show the effectiveness and rationality of each module designed in this paper. And the method reconstructs the details of contrast and color distribution, outperforms the current state-of-the-art methods in both qualitative and quantitative metrics, and shows excellent performance in the real world.

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Section: Fully Supervised Methodsmentioning

confidence: 99%

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Ma,

Fu,

Yang

et al. 2023

CMC

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A review of advances in image inpainting research

Liu³

et al. 2023

The Imaging Science Journal

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A dehazing method for flight view images based on transformer and physical priori

Ma,

Zhao,

Qin

2023

MBE

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<abstract><p>Aiming at the problems of local dehazing distortion and incomplete global dehazing of existing algorithms in real airborne cockpit environments, a two-stage dehazing method PhysiFormer combining physical a priori with a Transformer oriented flight perspective was proposed. The first stage used synthetic pairwise data to pre-train the dehazing model. First, a pyramid pooling module (PPM) was introduced in the Transformer for multiscale feature extraction to solve the problem of poor recovery of local details, then a global context fusion mechanism was used to enable the model to better perceive global information. Finally, considering that combining the physical a priori needs to rely on the estimation of the atmosphere light, an encoding-decoding structure based on the residual blocks was used to estimate the atmosphere light, which was then used for dehazing through the atmospheric scattering model for dehazing. The second stage used real images combined with physical priori to optimize the model to better fit the real airborne environment. The experimental results show that the proposed method has better naturalness image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) indexes and exhibits the best dehazing visual effect in the tests of dense haze, non-uniform haze and real haze images, which effectively improves the problems of color distortion and haze residue.</p></abstract>

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TPE: Lightweight Transformer Photo Enhancement Based on Curve Adjustment

Cited by 3 publications

References 38 publications

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

A review of advances in image inpainting research

A dehazing method for flight view images based on transformer and physical priori

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