Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟮𝟱𝟯 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔 𝗦𝘂𝗿𝘃𝗲𝘆 𝗼𝗻 𝗖𝗔𝗡𝗲𝘁: 𝗔 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗦𝗵𝗮𝗱𝗼𝘄 𝗥𝗲𝗺𝗼𝘃𝗮𝗹 𝗯𝘆 𝗝𝗗 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗔𝗺𝗲𝗿𝗶𝗰𝗮 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗠𝗼𝘂𝗻𝘁𝗮𝗶𝗻 𝗩𝗶𝗲𝘄, 𝗖𝗔, 𝗨𝗦𝗔 Follow me for a similar post: 🇮🇳 Ashish Patel 🇮🇳 Interesting Facts : 🔸 This paper is published ICCV2021. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/g_fYYiuA Code: https://lnkd.in/ge9UHW_m ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces.  🔸 At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential matching pairs of shadow and non-shadow patches. Combined with the potential contextual relationships between shadow and non-shadow regions, our well-designed contextual feature transfer (CFT) mechanism can transfer contextual information from non-shadow to shadow regions at different scales. With the reconstructed feature maps, we remove shadows at L and A/B channels separately.  🔸 At Stage-II, we use an encoder-decoder to refine current results and generate the final shadow removal results. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes. Extensive experimental results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts. #computervision #artificialintelligence #machinelearning