Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images(TIP18) 这是一篇单一图像对比度增强的论文,传统的单一图像对比度增强方法包括基于HE和Retinex理论,但由于自然场景的复杂性和单张图像包含的信息有限,往往很难产生高质量的结果。因此有了基于多曝光图像序列的图像增强,主要有多曝光图像融合(MEF...
An image processor comprises a contrast enhancer that multiplies a brightness- determining component (Y x,y) of an image element by a scale factor (k x,y). The scale factor (k x,y) is a function of an extreme value (P x,y) in a signal that is representative of the brightness- ...
[12]Artsiom Sanakoyeu, Dmytro Kotovenko, Sabine Lang, and Bjorn Ommer. A style-aware content loss for real-time hd style transfer. In ECCV, 2018. [13]Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. TIP, 2018. [14]Yub...
2018. Learning a deep single image contrast enhancer from multi-exposure images. IEEE TIP 27(4):2049-2062. B Cai,X Xu,K Guo,... - IEEE International Conference on Computer Vision 被引量: 0发表: 0年 Albumin Microbubble Echo-Contrast Material as an Enhancer for Ultrasound Accelerated Thrombol...
训练数据集来自四个公开可用的数据集:任务1使用 RoadScene(VIS-IR)和 Harvard2(PETMRI),任务2使用 Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images 的中的数据集,以及任务3使用Lytro。为了验证通用性,测试数据集还包含两个附加数据集:用于VIS-IR图像融合的 TNO 和用于多曝光图像融合的...
还有一种是估计一组中间的参数,然后依据中间参数来进行全局范围的增强,比如估计一个全局调整曲线(intensity transform),近几年我看到了好多全局参数的论文,有近十篇了吧,突出的像 3D-LUT、StarEnhancer、CSRNet等等,全局方法一是要求的数少,二是全局的操作,只要曲线不抖动,一般不会出现 artifacts,结果比较 natural。
enhancer = ImageEnhance.Contrast(brightened_image) final_image = enhancer.enhance(1.2) # 调整对比度为原来的1.2倍 # 显示原始图像和校正后的图像 image.show() final_image.show() # 保存校正后的图像 final_image.save('corrected_image.jpg')
Get creative with Pixlr’s online photo editing & design tools. Including AI image generator, batch editor, animation design, enhancer & more. Try now for FREE!
draw.point((x, y), fill=(255,255,255))#使用ImageEnhance可以增强图片的识别率enhancer=ImageEnhance.Contrast(image) image_enhancer= enhancer.enhance(4)printimage_to_string(image_enhancer) 分割字符串有多种方法 垂直像素直方图,假如图片宽度为100像素,则把图片切割为100个1像素的竖线,下面的红色部分为当前...
PET-MRI:Harvard,裁剪为120×120 VIF :TNO,裁剪为120×120 MEIF:来自 Learning a deep single image contrast enhancer from multi-exposure images,裁剪为120×120 MFIF:来自Multi-focus image fusion using dictionary-based sparse representation,裁剪为60×60 图像融合数据集链接 [图像融合常用数据集整理] ...