Inverse tone mapping, a technique to restore a high dynamic range (HDR) image from a single low dynamic range (LDR) image, exhibits wide versatility since it may be easily applied to any camera device. Besides, the recent advancement in deep learning has produced great performance improvement in the field of inverse tone mapping. However, it remains a difficult task to accurately restore a wide-range HDR image from a single LDR image. A recent study attempts a spatially adaptive exposure value (EV) condition generated from luminance values to create a pseudo-multi-exposure stack. However, by adopting only luminance values as input, the conditioning method cannot precisely reflect the input image information when generating the EV condition, resulting in the loss of color expression. Moreover, there are some concerns regarding how to apply the EV condition to the image feature. Thus, the key idea of this study is to directly adopt image features in generating EV conditions that are adaptive to both color and brightness. To do this, we design a condition generation network with an encoder-decoder structure and propose a novel multi-exposure stack generation network, which bidirectionally synthesizes the image features and EV-conditioned features. Additionally, to better preserve the feature information in the synthesis of the features, we propose a spatially-adaptive feature transformation block. Our proposed method exhibits outstanding results in restoring the multi-exposure stacks for HDR image synthesis. Furthermore, our method achieves state-of-the-art performance compared to existing methods in multi-exposure stack generation and stack-based HDR restoration.

With the success of Vision Transformer (ViT) in image classification, its variants have yielded great success in many downstream vision tasks. Among those, the semantic segmentation task has also benefited greatly from the advance of ViT variants. However, most studies of the transformer for semantic segmentation only focus on designing efficient transformer encoders, rarely giving attention to designing the decoder. Several studies make attempts in using the transformer decoder as the segmentation decoder with class-wise learnable query. Instead, we aim to directly use the encoder features as the queries. This paper proposes the Feature Enhancing Decoder transFormer (FeedFormer) that enhances structural information using the transformer decoder. Our goal is to decode the high-level encoder features using the lowest-level encoder feature. We do this by formulating high-level features as queries, and the lowest-level feature as the key and value. This enhances the high-level features by collecting the structural information from the lowest-level feature. Additionally, we use a simple reformation trick of pushing the encoder blocks to take the place of the existing self-attention module of the decoder to improve efficiency. We show the superiority of our decoder with various light-weight transformer-based decoders on popular semantic segmentation datasets. Despite the minute computation, our model has achieved state-of-the-art performance in the performance computation trade-off. Our model FeedFormer-B0 surpasses SegFormer-B0 with 1.8% higher mIoU and 7.1% less computation on ADE20K, and 1.7% higher mIoU and 14.4% less computation on Cityscapes, respectively. Code will be released at: https://github.com/jhshim1995/FeedFormer.

Accurate segmentation of the retina vessel is essential for the early diagnosis of eye-related diseases. Recently, convolutional neural networks have shown remarkable performance in retina vessel segmentation. However, the complexity of edge structural information and the changeable intensity distribution depending on retina images reduce the performance of the segmentation tasks. This paper proposes two novel deep learning-based modules, channel attention vision transformer (CAViT) and deep adaptive gamma correction (DAGC), to tackle these issues. The CAViT jointly applies the efficient channel attention (ECA) and the vision transformer (ViT), in which the channel attention module considers the interdependency among feature channels and the ViT discriminates meaningful edge structures by considering the global context. The DAGC module provides the optimal gamma correction value for each input image by jointly training a CNN model with the segmentation network so that all the retina images are mapped to a unified intensity distribution. The experimental results show that our proposed method achieves superior performance compared to conventional methods on widely used datasets, DRIVE and CHASE DB1.