【正文】 ssion of China Guardian Spring Auctions titled ―Fine Gilt—Bronze Buddhist Images‖ achieved a great deal with total sales volume of million yuan and 92 percent sale rate. The 5th Shamar Rinpoche Statue from 1617th century of Tibet was sold for million yuan. The Beijing Chengxuan Auctions featured almost 2,800 items of coins and stamps in three sessions with good sale rate. The Huachen Auctions also set a special session of photographs under the condition of largescale decline of auction sessions. There was a palpable dull thud of disappointment that acpanied the return of the imperial entourage of Zhen Huan to her homeland. It followed a couple years of hushed excitement as Chinese fans were fed tidbits about their proud concubine who was supposed to conquer the high ground of the North American market. Zhen Huan is, of course, the title character of The Legend of Zhen Huan, a 2022 television series that swept China off its feet and later took other Asian countries by storm. Two years ago, it was reported that HBO, a premium cable service headquartered in the United States, was going to air it in North America after some modification. Now, a condensed version that provides English subtitles but no dubbing has finally been made available on Netflix for online streaming. This version, highly anticipated as a milestone in China39。s Poly Theater. Their show, titled Ulan Muqir on the Grassland, depicted the history and development of the art troupe. Being from the region allowed me to embrace the culture of Inner Mongolia and being a member of the troupe showed me where I belonged, Nasun, the art troupe39。s performers of the troupe still tour the region39。t have a formal stage. The audience just sat on the grass. Usually, the performances became a big party with local people joining in. For him, the rewarding part about touring isn39。80s. We sat on the back of pickup trucks for hours. The sky was blue, and we couldn39。s Shaanxi province pass through a stop on the ancient Silk Road, Gansu39。 去噪和銳化仿真 結(jié)果 ： 原始圖像 X50 100 150 200 25050100150200250含噪聲的圖像 X n o i s e50 100 150 200 25050100150200250去噪后的圖像50 100 150 200 25050100150200250銳化去噪后的圖像50 100 150 200 25050100150200250 圖像壓縮 仿真 結(jié)果 ： 原始圖像50 100 150 200 25050100150200250分解后低頻和高頻信息100 200 300 400 500100200300400500第一次壓縮20 40 60 80 100 12020406080100120第二次壓縮20 40 60204060 Matlab 輸出： 壓縮前圖像 X 的大小： Name Size Bytes Class X 256x256 524288 double array Grand total is 65536 elements using 524288 bytes 第一次壓縮圖像的大小為： Name Size Bytes Class ca1 135x135 145800 double array Grand total is 18225 elements using 145800 bytes 第二次壓縮圖像的大小為： Name Size Bytes Class ca2 75x75 45000 double array Grand total is 5625 elements using 45000 bytes 圖像融合 仿真 結(jié)果 ： w b a r b50 100 150 200 25050100150200250w o m a n50 100 150 200 25050100150200250w b a r b 和 w o m a n 的融合圖像50 100 150 200 25050100150200250 3 結(jié)束語 本文詳細(xì)討論 了 小波在圖像處理領(lǐng)域的應(yīng)用， 并用 Matlab進(jìn)行了仿真， Matlab語言對(duì)數(shù)字圖像進(jìn)行處理時(shí)具有編程簡單、處理速度快的特點(diǎn)，結(jié)果顯示用小波變換理論進(jìn)行圖像處理可以取得較好的效果。wbarb 和 woman 的融合圖像 39。image(xx)。)。 %對(duì)融 合的系數(shù)進(jìn)行重構(gòu) xx=waverec2(c,s1,39。 %下面進(jìn)行小波變換域的圖像融合 c=c1+c2。sym439。 for i=1:sizec1(2) c1(i)=*c1(i)。)。 %用小波函數(shù) sym4 對(duì) X1 進(jìn)行 2 層小波分解 [c1,s1]=wavedec2(X1,2,39。woman39。colormap(map1)。 %畫出原始圖像 subplot(222)。 X1=X。)。 title(39。image(X2)。map2=map。) %圖像融合 figure(3)。 whos(39。第二次壓縮圖像的大小為： 39。)。 axis square title(39。image(ca2)。 %改變圖像的高度 ca2=*ca2。mat39。,2)。) %保留小波分解第二層低頻信息，進(jìn)行圖像的壓縮，此時(shí)壓縮比更大 %第二層的低頻信息即為 ca2，顯示第二層的低頻信息 ca2=appcoef2(c,s,39。 whos(39。第一次壓縮圖像的大小為： 39。)。 title(39。image(ca1)。 %改變圖像的高度 ca1=*ca1。mat39。,1)。 %保留小波分解第一層低頻信息，進(jìn)行圖像的壓縮 %第一層的低頻信息即為 ca1，顯示第一層的低頻信息 %首先對(duì)第一層信息進(jìn)行量化編碼 ca1=appcoef2(c,s,39。分解后低頻和高頻信息 39。image(c1)。v1,d1]。,1)。,c,s,39。 d1=wrcoef2(39。39。v39。,1)。,c,s,39。 h1=wrcoef2(39。39。a39。,c,s,1)。 cd1=detcoef2(39。v39。,c,s,1)。 ch1=detcoef2(39。39。)。) %對(duì)圖像用 小波進(jìn)行 2 層小波分解 [c,s]=wavedec2(X,2,39。 whos(39。壓縮前圖像 X 的大?。?39。)。colormap(map) title(39。 subplot(221)。)。title(39。image(wcodemat(blur2,192))。)。 end end %通過處理后的小 波系數(shù)重構(gòu) 圖像 blur2=waverec2(c,l,39。 if(abs(c(i))300) c(i)=c(i)*2。 csize=size(c)。db339。 %對(duì)去噪后的圖像做銳化處理 blur2=Xdenoise。去噪后的圖像 39。image(Xdenoise)。,2,thr,sorh,keepapp)。,c,s,39。 [Xdenoise,cxc,lxc,perf0,perfl2]=wdencmp(39。wv39。den39。)。 %用 sym5 小波對(duì)圖像信號(hào)進(jìn)行二層的小波分解 [c,s]=wavedec2(X,2,39。含噪聲的圖像 Xnoise39。image(wcodemat(Xnoise,192))。原始圖像 X39。image(wcodemat(X,192))。 figure(1)。 Xnoise=X+50*(rand(size(X)))。seed39。 Matlab 程序 如下： load wbarb %下面進(jìn)行噪聲的產(chǎn)生 init=3718025452。 3 MATLAB 仿真 下面 先 利用小波